Samples: Automatic updates to public repository

Remember to do the following:
    1. Ensure that modified/deleted/new files are correct
    2. Make this commit message relevant for the changes
    3. Force push
    4. Delete branch after PR is merged

If this commit is an update from one SDK version to another,
make sure to create a release tag for previous version.

README.md

@@ -1,6 +1,6 @@
 # Python samples
 
-This repository contains python code samples for Zivid SDK v2.12.0. For
+This repository contains python code samples for Zivid SDK v2.13.1. For
 tested compatibility with earlier SDK versions, please check out
 [accompanying
 releases](https://github.com/zivid/zivid-python-samples/tree/master/../../releases).

continuous-integration/setup.sh

@@ -28,7 +28,7 @@ function install_www_deb {
     rm -r $TMP_DIR || exit
 }
 
-install_www_deb "https://downloads.zivid.com/sdk/releases/2.12.0+6afd4961-1/u${VERSION_ID:0:2}/zivid_2.12.0+6afd4961-1_amd64.deb" || exit
+install_www_deb "https://downloads.zivid.com/sdk/releases/2.13.0+99a4ce9e-1/u${VERSION_ID:0:2}/zivid_2.13.0+99a4ce9e-1_amd64.deb" || exit
 
 python3 -m pip install --upgrade pip || exit
 python3 -m pip install --requirement "$ROOT_DIR/requirements.txt" || exit

source/applications/advanced/auto_2d_settings.py

@@ -520,10 +520,10 @@ def _print_poor_pixel_distribution(rgb: np.ndarray) -> None:
     black_and = np.sum(np.logical_and(np.logical_and(rgb[:, :, 0] == 0, rgb[:, :, 1] == 0), rgb[:, :, 2] == 0))
 
     print("Distribution of saturated (255) and black (0) pixels with final settings:")
-    print(f"Saturated pixels (at least one channel): {saturated_or}\t ({100*saturated_or/total_num_pixels:.2f}%)")
-    print(f"Saturated pixels (all channels):\t {saturated_and}\t ({100*saturated_and/total_num_pixels:.2f}%)")
-    print(f"Black pixels (at least one channel):\t {black_or}\t ({100*black_or/total_num_pixels:.2f}%)")
-    print(f"Black pixels (all channels):\t\t {black_and}\t ({100*black_and/total_num_pixels:.2f}%)")
+    print(f"Saturated pixels (at least one channel): {saturated_or}\t ({100 * saturated_or / total_num_pixels:.2f}%)")
+    print(f"Saturated pixels (all channels):\t {saturated_and}\t ({100 * saturated_and / total_num_pixels:.2f}%)")
+    print(f"Black pixels (at least one channel):\t {black_or}\t ({100 * black_or / total_num_pixels:.2f}%)")
+    print(f"Black pixels (all channels):\t\t {black_and}\t ({100 * black_and / total_num_pixels:.2f}%)")
 
 
 def _plot_image_with_histogram(rgb: np.ndarray, settings_2d: zivid.Settings2D) -> None:

source/applications/advanced/hand_eye_calibration/robodk_hand_eye_calibration/robodk_hand_eye_calibration.py

@@ -176,7 +176,7 @@ def generate_hand_eye_dataset(app: zivid.application, robot: Item, targets: List
         save_directory = _generate_directory()
         image_and_pose_iterator = 1
         while not image_and_pose_iterator > num_targets:
-            print(f"Capturing calibration object at robot pose {num_targets-len(targets)}")
+            print(f"Capturing calibration object at robot pose {num_targets - len(targets)}")
             _capture_one_frame_and_robot_pose(
                 robot,
                 camera,

source/applications/advanced/hand_eye_calibration/verify_hand_eye_with_visualization.py

@@ -120,7 +120,7 @@ def _path_list_creator(
         file_path = path / f"{file_prefix_name}{str(num).zfill(number_of_digits_zfill)}{file_suffix_name}"
         list_of_paths.append(file_path)
 
-        next_file_path = path / f"{file_prefix_name}{str(num+1).zfill(number_of_digits_zfill)}{file_suffix_name}"
+        next_file_path = path / f"{file_prefix_name}{str(num + 1).zfill(number_of_digits_zfill)}{file_suffix_name}"
 
         if not next_file_path.exists():
             return list_of_paths
@@ -162,7 +162,7 @@ def _main() -> None:
             list_of_open_3d_point_clouds = []
             for data_pair_id in range(number_of_dataset_pairs):
                 # Updating the user about the process status through the terminal
-                print(f"{data_pair_id} / {number_of_dataset_pairs} - {100*data_pair_id / number_of_dataset_pairs}%")
+                print(f"{data_pair_id} / {number_of_dataset_pairs} - {100 * data_pair_id / number_of_dataset_pairs}%")
 
                 # Reading point cloud from file
                 frame = zivid.Frame(list_of_paths_to_hand_eye_dataset_point_clouds[data_pair_id])

source/applications/advanced/verify_hand_eye_calibration/robodk_verify_hand_eye_calibration.py

@@ -66,6 +66,9 @@ def _get_robot_base_to_calibration_board_transform(
     Returns:
         robot_base_to_calibration_board_transform: A 4x4 numpy array containing the calibration board pose in the robot base frame
 
+    Raises:
+        ValueError: If an invalid calibration type is selected
+
     """
     if user_options.eih:
         print("Loading current robot pose")
@@ -75,12 +78,14 @@ def _get_robot_base_to_calibration_board_transform(
         robot_base_to_calibration_board_transform = (
             robot_base_to_flange_transform @ flange_to_camera_transform @ camera_to_calibration_board_transform
         )
-    if user_options.eth:
+    elif user_options.eth:
         robot_base_to_camera_transform = load_and_assert_affine_matrix(user_options.hand_eye_yaml)
 
         robot_base_to_calibration_board_transform = (
             robot_base_to_camera_transform @ camera_to_calibration_board_transform
         )
+    else:
+        raise ValueError("Invalid calibration type. Please choose either eye-in-hand or eye-to-hand.")
 
     return robot_base_to_calibration_board_transform
 

source/applications/point_cloud_tutorial.md

@@ -12,7 +12,6 @@ tutorial see:
 [**Introduction**](#Introduction) |
 [**Frame**](#Frame) |
 [**Point**](#Point-Cloud) |
-[**Transform**](#Transform) |
 [**Downsample**](#Downsample) |
 [**Normals**](#Normals) |
 [**Visualize**](#Visualize) |
@@ -148,7 +147,7 @@ In terms of memory allocation, there are two ways to copy data:
   - A user can pass a pointer to a pre-allocated memory buffer, and the
     Zivid SDK will copy the data to the pre-allocated memory buffer.
 
-## Transform
+-----
 
 You may want to
 [transform](https://support.zivid.com/latest//academy/applications/transform.html)
@@ -230,10 +229,10 @@ The size of normals is equal to the size of the input point cloud.
 
 Having the frame allows you to visualize the point cloud.
 
-No source available for {language\_name} You can visualize the point
+No source available for {language\_name}You can visualize the point
 cloud from the point cloud object as well.
 
-No source available for {language\_name} For more information, check out
+No source available for {language\_name}For more information, check out
 [Visualization
 Tutorial](https://support.zivid.com/latest/academy/applications/visualization-tutorial.html),
 where we cover point cloud, color image, depth map, and normals

source/camera/basic/capture_tutorial.md

@@ -433,7 +433,7 @@ frame.save(data_file_ply)
 
 We can get 2D color image from a 3D capture.
 
-No source available for {language\_name} 2D captures also produce 2D
+No source available for {language\_name}2D captures also produce 2D
 color images.
 
 ([go to

source/camera/info_util_other/firmware_updater.py

@@ -23,7 +23,7 @@ def _main() -> None:
             zivid.firmware.update(
                 camera,
                 progress_callback=lambda progress, description: print(
-                    f'{progress}% : {description}{("","...") [progress < 100]}'
+                    f'{progress}% : {description}{("","...")[progress < 100]}'
                 ),
             )
         else:

source/camera/maintenance/correct_camera_in_field.py

@@ -93,7 +93,7 @@ def _main() -> None:
 
         print(
             "If written to the camera, this correction can be expected to yield a dimension accuracy error of",
-            f"{accuracy_estimate.dimension_accuracy()*100:.3f}% or better in the range of z=[{accuracy_estimate.z_min():.3f}, {accuracy_estimate.z_max():.3f}] across the full FOV.",
+            f"{accuracy_estimate.dimension_accuracy() * 100:.3f}% or better in the range of z=[{accuracy_estimate.z_min():.3f}, {accuracy_estimate.z_max():.3f}] across the full FOV.",
             "Accuracy close to where the correction data was collected is likely better.",
         )
 

source/camera/maintenance/verify_camera_in_field.py

@@ -42,7 +42,7 @@ def _main() -> None:
     print(f"Successful measurement at {detection_result.centroid()}")
     camera_verification = zivid.experimental.calibration.verify_camera(infield_input)
     print(
-        f"Estimated dimension trueness error at measured position: {camera_verification.local_dimension_trueness()*100:.3f}%"
+        f"Estimated dimension trueness error at measured position: {camera_verification.local_dimension_trueness() * 100:.3f}%"
     )
 
 

source/camera/maintenance/verify_camera_in_field_from_zdf.py

@@ -55,7 +55,7 @@ def _main() -> None:
         print(f"Successful measurement at {detection_result.centroid()}")
         camera_verification = zivid.experimental.calibration.verify_camera(infield_input)
         print(
-            f"Estimated dimension trueness error at measured position: {camera_verification.local_dimension_trueness()*100:.3f}%"
+            f"Estimated dimension trueness error at measured position: {camera_verification.local_dimension_trueness() * 100:.3f}%"
         )